Determining Magnitudes of Calibration Stars

Determining Magnitudes of Calibration Stars SN 2014J in M82: Follow-Up Photometry Aims and Objectives This laboratory involved using photometry of the images of M28 which were obtained in R and I filters from ULO’s robotic telescope, hence determined the magnitudes of calibration stars and the new discovered supernova. In addition, compare the experimental results to the published AAVSO data by investigating the time series of data within the night of 23rd and 24th of January. Introduction Supernova are basically the explosion of massive supergiant star when they are reaching the end of their evolution. In general, supernovae can be classified as type I and type II supernova, each of them has its own specific features and dynamics. Type Ia supernova are generally thought to a white dwarf explosion and type II supernova are triggered by a core collapse of massive supergiant stars. Astronomers use type Ia supernova as a â€Å"standard candle† to measure the cosmic distances because they are very bright which can be seen at great distances and they all are observed to have similar brightness at their peaks. Light curve is one of the methods that astronomers use to identify different types of supernova. It is constructed by plotting the magnitude of the supernova as a function of time after the event of explosion. Type I supernova have very distinctive light curve compare to type II supernova. The figure bellow shows that type I supernova light curves exhibit a sharp increase to a maxima and then decrease steeply, eventually die away smoothly and gradually whereas type II supernova have less sharp peaks at their maxima and they have another peak later on after the first peak. (From hyperphysics.phy-astr.gsu.edu) Supernova SN2014J was discovered in the galaxy M82 on 21st January 2014 at ULO. Early studies of SN2014J light curve and spectrum indicate it is a type Ia supernova meaning that its light curve would have a peak absolute magnitude around the same value and it follows the general shape of type Ia light curve. In the laboratory, we can determine the magnitude of the supernova and plot the result to AAVSO database to see if it fits the published data and follow the trend of typical type Ia supernova light curves, whence to predict the peak brightness and its magnitude. Procedures I first downloaded and printed out the list of standard stars’ magnitudes from the AAVSO website which was used for reference throughout the laboratory. I also obtained the subset of the images of M82 from ULO’s robotic system by selecting only Rc and Ic filter’s image as well as the time range from 21:29 to 21:35 on the date of 23rd of January 2014. These images were opened and analysed with GAIA’s aperture photometry tool. I analysed the Ic images first in GAIA. Before making any magnitude measurements of stars, the brightest stars were checked to make sure they were not saturated and all the given star in AAVSO list don’t have peak count over 40000 counts. These must be done to get reliable results. In order to measure the magnitude, a suitable aperture needed to be drawn on the image and it should be in the size about two to three times the full width at half maximum (FWHM) of a star image. By using the slice tool, a graph of the selected star was opened in a new window where the FWHM could be measured. Determined FWHM of several listed stars and calculated the mean value, hence the aperture was set to three times the size of FWHM. When the aperture was set to a suitable size, I clicked the â€Å"define object aperture† button and placed the aperture to stars in the same order as they appeared in the AAVSO reference data. At last, dropped the aperture in supernova as well. After all, I collected my results by clicking on â€Å"Calculate results† button. These results were then save in a excel file for further analysis. Repeat the same procedures for Rc images and also save all the results in the same excel file. Measurements and results Calculated FWHM for making the judgement of the size of aperture: FWHM of star 112 = 178-174=4 FWHM of star 120 = 548-544=4 mean of FWHM = 4 size of aperture = 3 x 4 =12 Thus, I set the aperture as size 12 and placed it to each star to make a measurement. On my downloaded images for both Ic and Rc filter, star 155 and star 159 did not appear on them which lead to no measurements were taken from them. Results of Ic image and Rc image were tabulated in table 1 and table 2. Table 1. Results of Ic image Table 2. Results of Rc image Both of the tables have their first column listing all the known standard star from the reference data and supernova is also included. The column with heading of Sky is the values of sky annulus which are used to measure local background sky values for each star. And the last column is the signal from a star which is used to calculate magnitude and constant k of the star as well as the supernova. To estimate the magnitude of supernova, several calculations were carried out by using the equation: m = -2.5log N +k (1) The constant k was first calculated from known reference stars on the list and hence the mean value of constant k for both Ic and Rc imgaes. Then both Ic and Rc apparent magnitude of supernova were determined. All these values were tabulated in table 3 and associated uncertainties showed in table 4. Uncertainties on k and apparent magnitude were obtained by using the following equation: Ntot = N x n x 2.53 (2) Where N is the signal obtained from an average of n images, and 2.53 is called the ‘gain’ of the CCD. à ¢Ã‹â€ Ã¢â‚¬  m2 ≈ 1.1/√Ntot (3) Where à ¢Ã‹â€ Ã¢â‚¬  m2 is the error in magnitudes in measurement of the star signal And finally, the combined uncertainty becomes: (à ¢Ã‹â€ Ã¢â‚¬  mSN)2 = (à ¢Ã‹â€ Ã¢â‚¬  k)2 + (à ¢Ã‹â€ Ã¢â‚¬  m2)2 (4) Table 3. Results of calculated magnitude and constant k for both Ic and Rc imgaes Table 4. Uncertainties 4.3 Q5. Compare table 3 and the reference list, we can see that there is no obvious trend for constant k (both Ic and Rc) varies for standard star colours as well as standard-star magnitude. In the reference list, standard star colours such as B, V and B-V increase in their values following the order of the stars from 106 to 156, however, values of constant k fluctuates with different values of star colours but not follow any increasing or decreasing trend. Discussions and Conclusions This laboratory is to analyse and make magnitude measurements of the known stars from images of M28 which were obtained in R and I filters from ULO’s robotic telescope by using the aperture photometry tool, hence determined the magnitudes of the new discovered supernova. At the end of the laboratory, experimental results were plot in the light curves from AAVSO data so we can investigate the time series of data within the night of 23rd and 24th of January and more importantly, to predict the time that the supernova reach peak brightness and magnitude when Rc and Ic images were taken. Table 6. Results of magnitude both Rc and Ic by different students. 4.4 Q1. Table 6 shows all the laboratory results obtained by different groups of students. My calculated results for the Ic and Rc magnitude of supernova in the time range 21:29—21:35 are 10.04 +/- 0.02 and 10.54 +/- 0.03 respectively. Compare my results with those obtained by other students, I cannot conclude that out results are not consistence because the uncertainty for my Ic and Rc magnitude are +/-0.02 and +/- 0.03, but the difference in magnitude between students is about +/-0.1 which is not within my uncertainty range, thus the results did not maintain the consistency. 4.4 Q2. Because the great consistency of the results obtained from different groups of students, we can say the magnitude of the supernova is around the similar value at the time the images were taken. There is no obvious trend observed for the magnitude to change with time during that night, also, the condition of the sky was thought to be not vey ideal, thus the we cannot conclude that there is a change in brightness at the night of 23rd January to 24th January 2014. However, from the general shape of light curves of supernova, there should be obvious brightness change before and after peak. So, more reliable results should be obtained for plenty of nights to detect the change in brightness. 4.3.1 Q4. Table 4 listed majority of the uncertainties that can possibly be calculated from my results. They are relatively small compare to the large values of my results. However, there were also systematic uncertainties arising from the measurement of the background galaxy light. This is related to the size of annulus when I did the aperture measurements. The annulus is used to measure the background of the sky by drawing a larger circle and a smaller circle around the centre of the star. The background is therefore the region between these two apertures or the annulus. If the aperture is oversized, extra light from neighbour stars will be included which can cause unreliable results of the measurement as it introduces noise from the sky background, thus the signal strength from the star fluctuates as well. To check this uncertainty, I adjust the inner scale and the outer scale of the annulus to make some measurements of a star. The results were tabulated in table 5. Table 5. Results of aperture measurement with different size of annulus By trying different set of inner and outer annulus scales, the signals were very different to the one I obtained in the first stage. To mitigate them, multiple aperture with different size within the range about 2 to 3 times the FWHM can be used to make more measurement. This would give a more reliable signal strength of the star. Figure 1. Light curve of SN2014J from AAVSO with own data plotted Figure 1 is the light curve that I downloaded from AAVSO for SN 2014J with my results plotted on. My results for Ic and Rc magnitude were plotted in blue and yellow respectively. My data actually fits the AAVSO data, both of the magnitude are on the light curves at the same time on the night. We can spot that there is a trend from the AAVSO data from the same night at ULO. Both of I and R light curves show an increase in magnitude with change in time meaning that the supernova was brightening during the night at 23rd of January 2014. As mentioned previously in introduction, the shape of typical supernova Ia light curves exhibit a sharp increase to a maxima and then decrease steeply, eventually die away smoothly and gradually. Combine this information to the plot from AAVSO, there is a sharp increase from 17th to 23rd of January, and the magnitude gradually increase until reach its peak at around the date of 29th to 30th of January, and even the data is up to 4th February, we still can observe that there is a slight decrease in magnitude after 31th of January. We can say the magnitude at peak is about 9.5 for Ic and 10 for Rc. Throughout the whole observation, colour dependent extinction were not allowed by the Earth’s atmosphere. Basically, the extinction effect varies from night to night and the coefficient of extinction depends on wavelength and on different altitude. As the data was obtained from just one night, the extinction should not vary too much and for the wavelength dependent, different filter needed to be used to maintain a good accuracy of photometry, in this case, red and invisible filter were used. And the extinction is corrected by calculating the magnitudes outside the atmosphere. This can be the reason to cause the fluctuation of our measured magnitudes. In conclusion, I got the magnitude of SN2014J in Ic and Rc filter are 10.04 +/- 0.02 and 10.54 +/- 0.03 respectively within the time range 21:29—21:35 on the night of 23rd of January 2014. These data exactly follow the trend of the light curves of SN2014J obtained by AAVSO website at the same night. However, results gathered from other student in the same group didn’t show an obvious change in brightness of the supernova whereas the light curve did show that there is an increase in magnitude on the same date. References: American Association of Variable Star Observers AAVSO (2014) [Online] Available from http://www.aavso.org/ [Accessed: 11th February 2014] Aperturephotometry (2004) [Online] Available from http://mth.uct.ac.za/~lab/chap6/chap6/node3.html [Accessed: 11th February 2014] R Nave, HyperPhysics 2001 [Online] Available from: http://hyperphysics.phy-astr.gsu.edu/hbase/astro/snovcn.html [Accessed: 19th February 2014] Megan Gannon, How Students Discovered New Supernova in Nearby Galaxy (2014) [Online] lightAvailable from http://www.space.com/24413-supernova-star-explosion-student-discovery.html [Accessed: 19th February 2014] American Association of Variable Star Observers AAVSO (2014) [Online] Available from http://www.aavso.org/ [Accessed: 11th February 2014] Steve Fossey, PHAS1130 Practical Skill 1A SN 2014J in M82: Follow-up Photometry Manuals Shaoshan Zeng

Online Learning Environment Essay

The recent shift towards intensive use of computer technology in educational practices has already become so powerful that computer-based learning is no more perceived as something new. Whether it is computer laboratories in the primary schools or Internet-equipped computer facilities in the high-school, computers have turned into unalienable part of students’ life. However, such rapid and overwhelming spread of the new technology raised serious questions concerning positive and negative aspects, new opportunities for students and teachers, age peculiarities, and gender specifics of computer-based learning. The latter seems to be one of the major focuses of contemporary research in computer-based learning. Article â€Å"Gender differences in the use of computer mediated communication by post graduate distance students† written by Barrett and Lally (1999) investigates into the gender aspect of computer-mediated education, namely the use of Computer Mediated Communication (CMC) in a specific learning context by a small group of postgraduate (MEd) distance learners and their tutors. Taking into consideration the fact of steadily growing audience of students involved into online educational practices, relevance of such research can hardly be questioned. Although the number of participants is rather small – probably the first and foremost drawback of the research – the authors have managed to collect comprehensive amount of data using students’ electronic diaries, questionnaires and transcripts of all their online contributions. Barrett and Lally (1999) apply content analysis – arguably the most popular method in psychology – to evaluate on-line dialogues between members of the group. Specific attention has been paid to studying learning and socio-emotional pattern of behavior demonstrated by participants, since the major hypothesis of the authors is that male and female students differ significantly on this dimension. The choice of method seems absolutely correct keeping in mind the hypothesis posed by the authors. Key outcomes of the study supported the authors’ hypothesis: content analysis of the available data has revealed serious difference in behavior of men and women put into the online learning environment. Firstly, male students tend to send more messages than their female counterparts. Secondly, messages written by male students were on average twice as long as messages written by female students. And thirdly, male students tend to write socio-emotionally oriented messages, while female students exhibit interactive orientation. These three findings make it possible for the authors to establish strong correlation between student’s gender and his/her behavior in the online learning environment. Despite seeming plainness of this study, it seems rather difficult to give it a univocal assessment. On the one hand, there is a small sample group, which raises the issue of the study’s reliability, and simple research design, while on the other hand, there is exceptional relevance of the study (keeping in mind that it was published in 1999) and important findings, which have shown right direction for further research. Although there may be another point of view, the second component outweighs all drawbacks of the research: while the drawbacks relate to formal aspect of the study (e. g. insufficient sample size, poor data capturing techniques, etc), the content (hypothesis and findings) is really great. Newness of the author’s ideas has turned the article into a good starting point for other researchers, whose works help develop the most optimal learning and teaching strategies designed according to specific learning behavior of both sexes in a mixed online learning environment. Although newness is the most important characteristic of Barrett and Lally’s (1999) work, they apparently relied upon a number of previous studies. As soon as the overall subject of their research is concerned, the study can hardly be addressed as absolutely original: a number of studies dealing with the impact of computer technologies on the learning environment of classrooms (including the learning activities that take place in the classroom, and various educational strategies designed to make them more effective) were written throughout the 1990s. Although none of them paid serious attention to gender differences that might characterize behavior of students in computer learning environment, they definitely revealed a number of specific features of computer-based (including online) learning and teaching practices. For example, Richards (1996) conducted one of the first studies of the influence of computer-based environment on student motivations. His research conducted as a part of the Bell Atlantic’s World School Program revealed that computers and the Internet significantly improved students’ motivation. Thus, 92 percent of the respondents in Richards’ program classified the Internet as an effective learning tool (Richards, 1996). Findings of another research performed by Follansbee (1996) and his colleagues the same year stand in line with those of Richards. Comparison of the learning outcomes of students doing a task with access to the Internet demonstrated much higher level of confidence in conducting and presenting the research task. Besides, the study discovered that students with access to the Internet demonstrated better diversity and inventiveness presenting their tasks, could better integrate various perspectives, and presented their answers/projects more accurately (Follansbee 1996). In 1997, Proost and Lowyck (1997) published a study devoted to gender aspect of computer-based education. The sample group of the study was impressive: traditional and distance learning university students – a total of 1368 students (945 males and 416 females) over 18 years of age (Proost and Lowyck, 1997: 371-372). However, that study dealt only with the gender differences in perceptions of and preferences for computer based learning environments, while Barrett and Lally (1999) went deeper into the subject. Thus, Proost and Lowyck (1997) found out that female respondents had, on average, more negative perception of computer based technology and a preference for traditional methods than male respondents (p. 380), but did not try to answer the question whether male and female students demonstrate different behavioral patterns in the online learning environment or not. While Barrett and Lally (1999) relied on a number of previous researches, their own findings were further explored, confirmed, corrected, and extended by later studies. Kelly (2000) used Barrett and Lally’s (1999) assumption that girl pay more attention to the interactive aspect of computer-based communication and learning to explain why girls normally have little interest to computer science. In her account, computer does not involve a lot of teamwork and social interaction and, therefore, is less interesting than traditional communication and learning. In the same way Kelly (2000) explains why absolute majority of the computer games are designed according to tastes of male audiences and emphasize things (violence, intensive action, speed) that are not attractive to women. Therefore, boys feel more attraction to computers and computer-based learning and â€Å"†¦ with more males getting into careers in the computing industry, they perpetuate this cycle of catering for the needs of a predominantly male audience who gain an entry point into the computing world via games and later take up careers in the computing industry† (Kelly, 2000: 156). Similarly, Passing and Levin (2000) explored gender difference amongst pre-school students trying to reveal their preferences to various designs of multimedia learning interfaces (in order to improve outcomes of learning). The study involved a sample of 90 children (44 girls and 46 boys), and its major outcome was significant difference in boys and girls’ preferences: boys demonstrated more attraction to movement while girls paid more attention to visual elements. Shin and Chan (2004) also cite Barrett and Lally’s (1999) in their study of the effects of online learning on distance education students. The authors assume that there is a direct relationship between students’ involvement in online learning and distance learning outcomes taking into consideration gender aspect as the major factor that affects online learning (p. 277). Riding and Grimley (1999) investigated how differently the same computer multimedia affects cognitive style and performance of boys and girls (11 years). The study involved 40 boys and 40 girls and was conducted in two stages: firstly, participants underwent the procedure of assessment which revealed their cognitive style; secondly, they were offered a multi-choice recall test after studying a science topic with the help of computer and CD-ROM (p. 44-45). Upon completion of both stages the authors compared computer-based scores of the participants with scores given for similar topics learned with the help of traditional educational methods. The authors reported that â€Å"with regard to the mode of presentation of the multimedia materials, girls who were Wholist-Imagers and Analytic-Verbalisers were better with presentations which had picture and sound than those which had only picture and text. Those who were Wholist-Verbalizers and Analytic-Imagers were better with presentations which had picture and text than those which had only picture and sound. The results were the opposite for boys. For both gender groups performance was best with presentations which combined picture, text and sound† (Riding and Grimley 1999: 55). Evidently, the article written by Barrett and Lally (1999) can hardly be addressed as a classic work which has already become the basic reference point of modern studies in computer-based teaching and learning. At the same time, it is far from being a mediocre and irrelevant study that has absolutely not scientific value. The truth is somewhere in between: the article is a well-written piece of work which stands in line with previous research, has an element of newness and, therefore, opens new opportunities for scientists. REFERENCES Barrett, E and Lally, V. (1999) â€Å"Gender differences in an on-line learning environment† Journal of Computer Assisted Learning 15, 48-60 Follansbee, S. , Gilsdorf, N. , Stahl, S. , Dunfey, J. , Cohen, S. , Pisha, B. and Hughes, B. (1996) The role of online communication in schools: a national study . Peabody, MA: Center for Applied Special Technology. Kelly, Karen. (2000) â€Å"The Gender Gap: Why Do Girls Get Turned Off to Technology? † The Digital Classroom, ed. D. T. Gordon, The Harvard Education Letter, Cambridge, pp 154-160 O’Hara, S. P. (1998) â€Å"A case study of attitudinal effects of Internet use in a middle school integrated science curriculum†. Paper presented at the Annual Meeting of the National Association for Research in Science Teaching, San Diego, CA, April 19-22, 1998 (Eric document ED417978). Passing, D. & Levin, H. (2000) â€Å"Gender Preferences for Multimedia interfaces†, Journal of Computer Assisted Learning, 16: 64-71 Proost, K. , Elen J. & Lowyck J. (1997) â€Å"Effects of Gender on Perceptions of and Preferences for Telematic Learning Environments†, Journal of Research on Computing in Education, Summer, 29(4): 370-384 Richards, F. C. (1996) â€Å"The impact of the Internet on teaching and learning as perceived by teachers, library media specialists and students† Masters thesis, Salem-Teikyo University (Eric document ED410943)

Synthesis And Testing Of Aa2014 Al2o3 - 1962 Words

Synthesis And Testing of AA2014-Al2O3(alpha) Nano Composite Abstract—Al2O3 reinforced Aluminium Matrix Composites give advanced mechanical physical properties. They have voluminous applications in automobile, aerospace, defense, sports, electronics, bio-medical and other industrial fields. Various manufacturing techniques like stir casting, ultra-sonic assisted casting, compo-casting, powder metallurgy, liquid infiltration are used for the production of the aluminium matrix composites. These composites reveal advanced physical and mechanical properties like lower density, low coefficient of thermal expansion, good corrosion resistance, high tensile strength, high stiffness, high hardness and wear resistance. Our paper reviews the characterization of mechanical properties like tensile strength, hardness with production route of casting for aluminium matrix- Al2O3 composites. Al2O3 agglomeration along with the increasing volume percentage is still a challenging task in composites materials manufacturing. 0.5% by weight of pure magnesium powder is used as wetting agent. Process parameters are optimized for the stir casting process of aluminium composite. Tensile and hardness test microstructure study was performed to find the deviation from the aluminium alloy. Keywords: Agglomeration, stir casting, aluminium composite. I. INTRODUCTION The use of Aluminium alloys in aerospace and automobile industries are remarkable due to their low density and good mechanical properties.

Edna Pontellier’s Search for Independence in Chopins The...

Ednas Search for Independence in The Awakening How do you honor the deepest truth you know? --Ram Das In order to honor ones deepest truth, one must first discover what that truth is and then apply that truth to everyday life. The life of Edna Pontellier in The Awakening signifies the search, discovery, and application of an individuals deepest truth. Edna, a wealthy New Orleans housewife, at first attempts to find the deepest truth about herself by conforming to societys norms. She marries a well-respected man, Leonce, and bears him children. However, Edna discovers that she wants more out of life; something about her marriage is not allowing her to achieve fulfillment. Through her relationships,†¦show more content†¦But they need not have thought that they could possess her, body and soul (504). Although Edna did perform her duties as a wife for some time, she is not the typical housewife. She does not worship her husband or idolize her children, which makes both Edna and Leonce begin to sense that Edna i s different from the other mother-women (Lin 1). Edna never realized the reasons she neglected her duties as a wife until she fell in love with Robert and acknowledged that her desires and needs exist outside of her marriage. Thus, after her experiences with Robert, Edna is ready to neglect her husband even more, because she now realizes that her husband is holding her back from her needs. When Leonce tries to make Edna act like the other women that obey their husbands, his attempts to control Edna further instigate Ednas desire for independence from him. For example, the scene when Edna is lying in the hammock, Leonce says: I cant permit you to stay out there all night. You must come in the house instantly, Edna replies: I mean to stay out here. I dont wish to go in, and I dont intend to. Dont speak to me like that again; I shall not answer you (492). 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